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The Influence of First Year Behaviour in the Progressions of University Students

  • R. Campagni
  • D. MerliniEmail author
  • M. C. Verri
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 865)

Abstract

Advanced clustering techniques are used on educational data concerning various cohorts of university students. First, K-means analysis is used to classify students according to the results of the self assessment test and the first year performance. Then, the analysis concentrates on the subset of the data involving the cohorts of students for which the behavior during the first, second and third year of University is known. The results of the second and third year are analyzed and the students are re-assigned to the clusters obtained during the analysis of the first year. In this way, for each student we are able to obtain the sequence of traversed clusters during three years, based on the results achieved during the first. For the data set under analysis, this analysis highlights three groups of students strongly affected by the results of the first year: high achieving students who start high and maintain their performance over the time, medium-high achieving students throughout the entire course of study and, low achieving students unable to improve their performance who often abandon their studies. This kind of study can be used by the involved laurea degree to detect critical issues and undertake improvement strategies.

Keywords

Educational data mining Clustering Student progressions Self assessment test 

References

  1. 1.
    Baker, R.S.J.D.: Educational data mining: an advance for intelligent systems in education. IEEE Intell. Syst. 29(3), 78–82 (2014)CrossRefGoogle Scholar
  2. 2.
    Bower, A.J.: Analyzing the longitudinal K-12 grading histories of entire cohorts of students: grades, data driven decision making, dropping out and hierarchical cluster analysis. Pract. Assess. Res. Eval. 15(7), 1–18 (2010)Google Scholar
  3. 3.
    Campagni, R., Merlini, D., Sprugnoli, R., Verri, M.C.: Data mining models for student careers. Expert Syst. Appl. 42(13), 5508–5521 (2015)CrossRefGoogle Scholar
  4. 4.
    Campagni, R., Merlini, D., Verri, M.C.: University student progressions and first year behaviour. In: Proceedings of CSEDU 2017 - the 9th International Conference on Computer Supported Education, vol. 2, pp. 46–56 (2017)Google Scholar
  5. 5.
    Kabakchieva, D., Stefanova, K., Kisimov, V.: Determining student profiles and predicting performance. In: Proceedings of EDM 2011, 4th International Conference on Educational Data Mining, Eindhoven, The Netherlands (2011)Google Scholar
  6. 6.
    Natek, S., Zwilling, M.: Student data mining solution-knowledge management system related to higher education institutions. Expert Syst. Appl. 41, 6400–6407 (2014)CrossRefGoogle Scholar
  7. 7.
    Peña-Ayala, A.: Educational data mining: a survey and a data mining-based analysis. Expert Syst. Appl. 41, 1432–1462 (2014)CrossRefGoogle Scholar
  8. 8.
    Romero, C., Romero, J.R., Ventura, S.: A survey on pre-processing educational data. In: Peña-Ayala, A. (ed.) Educational Data Mining. SCI, vol. 524, pp. 29–64. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-02738-8_2CrossRefGoogle Scholar
  9. 9.
    Romero, C., Ventura, S.: Data mining in education. Wiley Interdisc. Rev. Data Min. Knowl. Discov. 3(1), 12–27 (2013)CrossRefGoogle Scholar
  10. 10.
    Tan, P.N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Addison-Wesley, Boston (2006)Google Scholar
  11. 11.
    Witten, I.H., Frank, E., Hall, M.A.: Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn. Morgan Kaufmann, Burlington (2011)Google Scholar
  12. 12.
    Zimmermann, J., Brodersen, K.H., Heinimann, H.R., Buhmann, J.M.: A model-based approach to predicting graduate-level performance using indicators of undergraduate-level performance. J. Educ. Data Min. 7(3), 151–176 (2015)Google Scholar
  13. 13.
    Zimmermann, J., Brodersen, K.H., Pellet, J.P., August, E., Buhmann, J.M.: Predicting graduate level performance from undergraduate achievements. In: Proceedings of EDM 2011, 4th International Conference on Educational Data Mining, Eindhoven, The Netherlands (2011)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Dipartimento di Statistica, Informatica, ApplicazioniFlorenceItaly

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